Bayesian Inference

Practical Variational Inference for Neural Networks NeurIPS 2011 Predicting response time and error rates in visual search NeurIPS 2011 Priors over Recurrent Continuous Time Processes NeurIPS 2011 Probabilistic amplitude and frequency demodulation NeurIPS 2011 Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models JMLR 2011 Quasi-Newton Methods for Markov Chain Monte Carlo NeurIPS 2011 Query-Aware MCMC NeurIPS 2011 Revisiting MAP Estimation, Message Passing and Perfect Graphs AISTATS 2011 Robust Bayesian Matrix Factorisation AISTATS 2011 Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity COLT 2011 Select and Sample - A Model of Efficient Neural Inference and Learning NeurIPS 2011 Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC NeurIPS 2011 Sparse Bayesian Multi-Task Learning NeurIPS 2011 Sparse Estimation with Structured Dictionaries NeurIPS 2011 Sparse Linear Identifiable Multivariate Modeling JMLR 2011 Spatial distance dependent Chinese restaurant processes for image segmentation NeurIPS 2011 Switch-Reset Models : Exact and Approximate Inference AISTATS 2011 t-divergence Based Approximate Inference NeurIPS 2011 Testing a Bayesian Measure of Representativeness Using a Large Image Database NeurIPS 2011 The Doubly Correlated Nonparametric Topic Model NeurIPS 2011 The Indian Buffet Process: An Introduction and Review JMLR 2011 The Kernel Beta Process NeurIPS 2011 Theoretical Analysis of Bayesian Matrix Factorization JMLR 2011 The Sample Complexity of Self-Verifying Bayesian Active Learning AISTATS 2011 Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction NeurIPS 2011